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Competence-Preserving Retention of Learned Knowledge in Soar's Working and Procedural Memories

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Competence-Preserving Retention of Learned Knowledge in Soar's Working and Procedural Memories Competence-Preserving Retention of Learned Knowledge in Soar’s Working and Procedural Memories Nate Derbinsky ([email protected]) John E. Laird ([email protected]) University of Michigan, 2260 Hayward Street Ann Arbor, MI 48109-2121 USA Abstract amounts of information from the environment, and where over time this learned knowledge overwhelms reasonable Effective management of learned knowledge is a challenge when modeling human-level behavior within complex, computational limits. In response, we present and evaluate temporally extended tasks. This paper evaluates one approach novel policies for selective retention of learned knowledge to this problem: forgetting knowledge that is not in active use in the working and procedural memories of Soar. These (as determined by base-level activation) and can likely be policies investigate a common hypothesis: it is rational for reconstructed if it becomes relevant. We apply this model for the architecture to forget a unit of knowledge when there is selective retention of learned knowledge to the working and a high degree of certainty that it is not of use, as calculated procedural memories of Soar. When evaluated in simulated, robotic exploration and a competitive, multi-player game, by base-level activation (Anderson et al., 2004), and that it these policies improve model reactivity and scaling while can be reconstructed in the future if it becomes relevant. We maintaining reasoning competence. demonstrate that these task-independent policies improve model reactivity and scaling, while maintaining problem- Keywords: large-scale cognitive modeling; working memory; procedural memory; cognitive architecture; Soar solving competence. Introduction Related Work Typical cognitive models persist for short periods of time Previous cognitive-modeling research has investigated (seconds to a few minutes) and have modest learning forgetting in order to account for human behavior and requirements. For these models, current cognitive experimental data. As a prominent example, memory decay architectures, such as Soar (Laird, 2012) and ACT-R has long been a core commitment of the ACT-R theory (Anderson et al., 2004), executing on commodity computer (Anderson et al., 2004), as it has been shown to account for systems, are sufficient. However, prior work (Kennedy & a class of memory retrieval errors (Anderson et al., 1996). Trafton, 2007) has shown that cognitive models of complex, Similarly, research in Soar investigated task-performance protracted tasks can accumulate large amounts of effects of forgetting short-term (Chong, 2003) and knowledge, and that the computational performance of procedural (Chong, 2004) knowledge. By contrast, the existing architectures degrades as a result. motivation for and outcome of this work is to investigate the This issue, where more knowledge can harm problem- degree to which selective retention can support long-term, solving performance, has been dubbed the utility problem, real-time modeling in complex tasks. and has been studied in many contexts, such as explanation- Prior work shows the potential for cognitive benefits of based learning (Minton, 1990; Tambe et al., 1990), case- memory decay, such as in task-switching (Altmann & Gray, based reasoning (Smyth & Keane, 1995; Smyth & 2002) and heuristic inference (Schooler & Hertwig, 2005). Cunningham, 1996), and language learning (Daelemans et In this paper, we focus on improved reactivity and scaling. al., 1999). Markovitch and Scott (1988) have characterized We extend prior investigations of long-term symbolic different strategies for dealing with the utility problem in learning in Soar (Kennedy & Trafton, 2007), where the terms of information filters applied at different stages in the source of learning was primarily from internal problem problem-solving process. One common strategy that is solving. In this paper, the evaluation domains accumulate relevant to cognitive modeling is selective retention, or information from interaction with an external environment. forgetting, of learned knowledge. The benefit of this approach, as opposed to selective utilization, is that all The Soar Cognitive Architecture available knowledge is brought to bear on problem solving, Soar is a cognitive architecture that has been used for a property that is crucial for model competence in complex developing intelligent agents and modeling human tasks. However, it can be challenging to devise forgetting cognition. Historically, one of Soar’s main strengths has policies that work well across a variety of problem domains, been its ability to efficiently represent and bring to bear effectively balancing the task performance of cognitive large amounts of symbolic knowledge to solve diverse models with reductions in retrieval time and storage problems using a variety of methods (Laird, 2012). requirements of learned knowledge. Figure 1 shows the structure of Soar. At the center is a In context of this challenge, we present two tasks where symbolic working memory that represents the agent’s effective behavior requires that the model accumulate large current state. It is here that perception, goals, retrievals from This distinction is relevant to forgetting productions. When an RL rule that was learned via chunking is updated, that rule is no longer redundant with the knowledge that led to its creation, as it now incorporates information from environmental interaction that was not captured in the original subgoal processing. Soar incorporates two long-term declarative memories, semantic and episodic (Derbinsky & Laird, 2010). Semantic memory stores working-memory objects, independent of overall working-memory connectivity (Derbinsky, Laird, & Smith, 2010), and episodic memory incrementally encodes and temporally indexes snapshots of working memory, Figure 1: The Soar cognitive architecture. resulting in an autobiographical history of agent experience long-term memory, external action directives, and structures (Derbinsky & Laird, 2009). Agents retrieve knowledge from from intermediate reasoning are jointly represented as a one of these memory systems by constructing a symbolic connected, directed graph. The primitive representational cue in working memory; the intended memory system then unit of knowledge in working memory is a symbolic triple interprets the cue, searches its store for the best matching (identifier, attribute, value), termed a working-memory memory, and if it finds a match, reconstructs the associated element, or WME. The first symbol of a WME (identifier) knowledge in working memory. For episodic memory, the must be an existing node in the graph, whereas the second time to reconstruct knowledge depends on the size of (attribute) and third (value) symbols may be either terminal working memory at the time of encoding, another constants or non-terminal graph nodes. Multiple WMEs that motivation for a concise agent state. share the same identifier are termed an “object,” and the set Agent reasoning in Soar consists of a sequence of of individual WMEs sharing that identifier are termed decisions, where the aim of each decision is to select and “augmentations” of that object. apply an operator in service of the agent’s goal(s). The Procedural memory stores the agent’s knowledge of when primitive decision cycle consists of the following phases: and how to perform actions, both internal, such as querying encode perceptual input; fire rules to elaborate agent state, long-term declarative memories, and external, such as as well as propose and evaluate operators; select an controlling robotic actuators. Knowledge in this memory is operator; fire rules that apply the operator; and then process represented as if-then rules. The conditions of rules test output directives and retrievals from long-term memory. patterns in working memory and the actions of rules add Unlike ACT-R, multiple rules may fire in parallel during a and/or remove working-memory elements. Soar makes use single phase. The time to execute the decision cycle, which of the Rete algorithm for efficient rule matching (Forgy, primarily depends on the speed with which the architecture 1982) and retrieval time scales to large stores of procedural can match rules and retrieve knowledge from episodic and knowledge (Doorenbos, 1995). However, the Rete algorithm semantic memories, determines agent reactivity. We have is known to scale linearly with the number of elements in found that 50 msec. is an acceptable upper bound on this working memory, a computational issue that motivates response time across numerous domains, including robotics, maintaining a relatively small working memory. video games, and human-computer interaction (HCI) tasks. Soar learns procedural knowledge via chunking (Laird et There are two types of persistence for working-memory al., 1986) and reinforcement learning (RL; Nason & Laird, elements added as the result of rule firing. Rules that fire to 2005) mechanisms. Chunking creates new productions: it apply a selected operator create operator-supported converts deliberate subgoal processing into reactive rules by structures. These WMEs will persist in working memory compiling over production-firing traces, a form of until deliberately removed. In contrast, rules that do not test explanation-based learning (EBL). If subgoal processing a selected operator create instantiation-supported structures, does not interact with the environment, the chunked rule is which persist only as long as the rules that created them redundant with existing knowledge and serves
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